Overview

Dataset statistics

 Our DatasetCollected Dataset
Number of variables119
Number of observations41583930
Missing cells00
Missing cells (%)0.0%0.0%
Duplicate rows00
Duplicate rows (%)0.0%0.0%
Total size in memory357.5 KiB276.5 KiB
Average record size in memory88.0 B72.0 B

Variable types

 Our DatasetCollected Dataset
Categorical42
Numeric77

Alerts

Our DatasetCollected Dataset
Ia is highly overall correlated with Vb and 2 other fieldsIa is highly overall correlated with GHigh Correlation
Ib is highly overall correlated with Va and 2 other fieldsIb is highly overall correlated with CHigh Correlation
Ic is highly overall correlated with CIc is highly overall correlated with CHigh Correlation
Va is highly overall correlated with Ib and 1 other fieldsAlert not present in this datasetHigh Correlation
Vb is highly overall correlated with Ia and 1 other fieldsVb is highly overall correlated with Vc and 1 other fieldsHigh Correlation
Vc is highly overall correlated with Ia and 3 other fieldsVc is highly overall correlated with Vb and 2 other fieldsHigh Correlation
Fault is highly overall correlated with GFault is highly overall correlated with GHigh Correlation
G is highly overall correlated with FaultG is highly overall correlated with Ia and 2 other fieldsHigh Correlation
C is highly overall correlated with IcC is highly overall correlated with Ib and 3 other fieldsHigh Correlation
B is highly overall correlated with IbAlert not present in this datasetHigh Correlation
A is highly overall correlated with IaAlert not present in this datasetHigh Correlation
Ia has unique values Ia has unique values Unique
Ib has unique values Ib has unique values Unique
Ic has unique values Ic has unique values Unique
Va has unique values Va has unique values Unique
Vb has unique values Vb has unique values Unique
Fault has 180 (4.3%) zeros Fault has 1182 (30.1%) zeros Zeros

Reproduction

 Our DatasetCollected Dataset
Analysis started2023-11-24 16:04:20.7385392023-11-24 16:04:42.529420
Analysis finished2023-11-24 16:04:42.5248122023-11-24 16:04:57.769301
Duration21.79 seconds15.24 seconds
Software versionydata-profiling vv4.6.1ydata-profiling vv4.6.1
Download configurationconfig.jsonconfig.json

Variables

G
Categorical

 Our DatasetCollected Dataset
Distinct22
Distinct (%)< 0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Memory size32.6 KiB30.8 KiB
1
2970 
0
1188 
0
2232 
1
1698 

Length

 Our DatasetCollected Dataset
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 Our DatasetCollected Dataset
Total characters41583930
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 Our DatasetCollected Dataset
Unique00 ?
Unique (%)0.0%0.0%

Sample

 Our DatasetCollected Dataset
1st row00
2nd row01
3rd row10
4th row11
5th row00

Common Values

ValueCountFrequency (%)
1 2970
71.4%
0 1188
 
28.6%
ValueCountFrequency (%)
0 2232
56.8%
1 1698
43.2%

Length

2023-11-24T21:34:58.085144image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Our Dataset

2023-11-24T21:34:58.456375image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:58.742409image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 2970
71.4%
0 1188
 
28.6%
ValueCountFrequency (%)
0 2232
56.8%
1 1698
43.2%

Most occurring characters

ValueCountFrequency (%)
1 2970
71.4%
0 1188
 
28.6%
ValueCountFrequency (%)
0 2232
56.8%
1 1698
43.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4158
100.0%
ValueCountFrequency (%)
Decimal Number 3930
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2970
71.4%
0 1188
 
28.6%
ValueCountFrequency (%)
0 2232
56.8%
1 1698
43.2%

Most occurring scripts

ValueCountFrequency (%)
Common 4158
100.0%
ValueCountFrequency (%)
Common 3930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2970
71.4%
0 1188
 
28.6%
ValueCountFrequency (%)
0 2232
56.8%
1 1698
43.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4158
100.0%
ValueCountFrequency (%)
ASCII 3930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2970
71.4%
0 1188
 
28.6%
ValueCountFrequency (%)
0 2232
56.8%
1 1698
43.2%

C
Categorical

 Our DatasetCollected Dataset
Distinct22
Distinct (%)< 0.1%0.1%
Missing00
Missing (%)0.0%0.0%
Memory size32.6 KiB30.8 KiB
1
2365 
0
1793 
0
2314 
1
1616 

Length

 Our DatasetCollected Dataset
Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

 Our DatasetCollected Dataset
Total characters41583930
Distinct characters22
Distinct categories11 ?
Distinct scripts11 ?
Distinct blocks11 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

 Our DatasetCollected Dataset
Unique00 ?
Unique (%)0.0%0.0%

Sample

 Our DatasetCollected Dataset
1st row10
2nd row10
3rd row00
4th row00
5th row10

Common Values

ValueCountFrequency (%)
1 2365
56.9%
0 1793
43.1%
ValueCountFrequency (%)
0 2314
58.9%
1 1616
41.1%

Length

2023-11-24T21:34:59.040172image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

Our Dataset

2023-11-24T21:34:59.341399image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:59.599950image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 2365
56.9%
0 1793
43.1%
ValueCountFrequency (%)
0 2314
58.9%
1 1616
41.1%

Most occurring characters

ValueCountFrequency (%)
1 2365
56.9%
0 1793
43.1%
ValueCountFrequency (%)
0 2314
58.9%
1 1616
41.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4158
100.0%
ValueCountFrequency (%)
Decimal Number 3930
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2365
56.9%
0 1793
43.1%
ValueCountFrequency (%)
0 2314
58.9%
1 1616
41.1%

Most occurring scripts

ValueCountFrequency (%)
Common 4158
100.0%
ValueCountFrequency (%)
Common 3930
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2365
56.9%
0 1793
43.1%
ValueCountFrequency (%)
0 2314
58.9%
1 1616
41.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4158
100.0%
ValueCountFrequency (%)
ASCII 3930
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2365
56.9%
0 1793
43.1%
ValueCountFrequency (%)
0 2314
58.9%
1 1616
41.1%

B
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 KiB
1
2129 
0
2029 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4158
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 2129
51.2%
0 2029
48.8%

Length

2023-11-24T21:34:59.905879image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 2129
51.2%
0 2029
48.8%

Most occurring characters

ValueCountFrequency (%)
1 2129
51.2%
0 2029
48.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4158
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2129
51.2%
0 2029
48.8%

Most occurring scripts

ValueCountFrequency (%)
Common 4158
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2129
51.2%
0 2029
48.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4158
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2129
51.2%
0 2029
48.8%

A
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size32.6 KiB
1
2569 
0
1589 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters4158
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 2569
61.8%
0 1589
38.2%

Length

2023-11-24T21:35:00.233338image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 2569
61.8%
0 1589
38.2%

Most occurring characters

ValueCountFrequency (%)
1 2569
61.8%
0 1589
38.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4158
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2569
61.8%
0 1589
38.2%

Most occurring scripts

ValueCountFrequency (%)
Common 4158
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2569
61.8%
0 1589
38.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4158
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2569
61.8%
0 1589
38.2%

Ia
Real number (ℝ)

 Our DatasetCollected Dataset
Distinct41583930
Distinct (%)100.0%100.0%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean-13.69035111.46372
 Our DatasetCollected Dataset
Minimum-373.1524-883.54232
Maximum659.97275885.54698
Zeros00
Zeros (%)0.0%0.0%
Negative29991957
Negative (%)72.1%49.8%
Memory size32.6 KiB30.8 KiB
2023-11-24T21:35:00.914531image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

 Our DatasetCollected Dataset
Minimum-373.1524-883.54232
5-th percentile-225.30528-818.74226
Q1-79.925649-138.007
median-18.0419770.88517926
Q35.3981122225.59069
95-th percentile306.67198846.03826
Maximum659.97275885.54698
Range1033.12511769.0893
Interquartile range (IQR)85.323761363.5977

Descriptive statistics

 Our DatasetCollected Dataset
Standard deviation156.97827467.35288
Coefficient of variation (CV)-11.46634440.767997
Kurtosis5.6615711-0.42841512
Mean-13.69035111.46372
Median Absolute Deviation (MAD)35.977598184.68374
Skewness1.84286270.023595775
Sum-56924.47945052.418
Variance24642.178218418.72
MonotonicityNot monotonicNot monotonic
2023-11-24T21:35:01.557814image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-18.57624692 1
 
< 0.1%
-196.524354 1
 
< 0.1%
4.178688918 1
 
< 0.1%
-15.39002545 1
 
< 0.1%
-56.74951427 1
 
< 0.1%
-18.09885706 1
 
< 0.1%
-8.90408023 1
 
< 0.1%
-68.7888426 1
 
< 0.1%
-101.9865122 1
 
< 0.1%
-214.8537626 1
 
< 0.1%
Other values (4148) 4148
99.8%
ValueCountFrequency (%)
-25.96680477 1
 
< 0.1%
499.8638951 1
 
< 0.1%
168.6653879 1
 
< 0.1%
552.3765666 1
 
< 0.1%
37.53980142 1
 
< 0.1%
713.7109093 1
 
< 0.1%
-330.6504525 1
 
< 0.1%
-56.79977885 1
 
< 0.1%
-37.3162208 1
 
< 0.1%
4.583159814 1
 
< 0.1%
Other values (3920) 3920
99.7%
ValueCountFrequency (%)
-373.152398 1
< 0.1%
-373.0150815 1
< 0.1%
-372.7563352 1
< 0.1%
-369.7578019 1
< 0.1%
-369.7464044 1
< 0.1%
-368.1862297 1
< 0.1%
-367.8124215 1
< 0.1%
-367.5008224 1
< 0.1%
-366.916855 1
< 0.1%
-365.3396922 1
< 0.1%
ValueCountFrequency (%)
-883.5423165 1
< 0.1%
-883.528982 1
< 0.1%
-883.4823482 1
< 0.1%
-883.2043858 1
< 0.1%
-883.1904127 1
< 0.1%
-882.995539 1
< 0.1%
-882.8266322 1
< 0.1%
-882.6689661 1
< 0.1%
-882.5282922 1
< 0.1%
-882.4019447 1
< 0.1%
ValueCountFrequency (%)
-883.5423165 1
< 0.1%
-883.528982 1
< 0.1%
-883.4823482 1
< 0.1%
-883.2043858 1
< 0.1%
-883.1904127 1
< 0.1%
-882.995539 1
< 0.1%
-882.8266322 1
< 0.1%
-882.6689661 1
< 0.1%
-882.5282922 1
< 0.1%
-882.4019447 1
< 0.1%
ValueCountFrequency (%)
-373.152398 1
< 0.1%
-373.0150815 1
< 0.1%
-372.7563352 1
< 0.1%
-369.7578019 1
< 0.1%
-369.7464044 1
< 0.1%
-368.1862297 1
< 0.1%
-367.8124215 1
< 0.1%
-367.5008224 1
< 0.1%
-366.916855 1
< 0.1%
-365.3396922 1
< 0.1%

Ib
Real number (ℝ)

 Our DatasetCollected Dataset
Distinct41583930
Distinct (%)100.0%100.0%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean-102.75636-35.470323
 Our DatasetCollected Dataset
Minimum-796.38732-900.52695
Maximum64.494205889.78811
Zeros00
Zeros (%)0.0%0.0%
Negative26971891
Negative (%)64.9%48.1%
Memory size32.6 KiB30.8 KiB
2023-11-24T21:35:02.168436image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

 Our DatasetCollected Dataset
Minimum-796.38732-900.52695
5-th percentile-620.60008-827.9214
Q1-94.998198-234.46517
median-8.93821018.3203346
Q36.017072993.27316
95-th percentile22.40597783.56099
Maximum64.494205889.78811
Range860.881521790.3151
Interquartile range (IQR)101.01527327.73833

Descriptive statistics

 Our DatasetCollected Dataset
Standard deviation201.07642436.17255
Coefficient of variation (CV)-1.9568271-12.296831
Kurtosis2.3405579-0.13415959
Mean-102.75636-35.470323
Median Absolute Deviation (MAD)18.626656103.5386
Skewness-1.8922849-0.051048103
Sum-427260.93-139398.37
Variance40431.726190246.49
MonotonicityNot monotonicNot monotonic
2023-11-24T21:35:02.747790image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.60813154 1
 
< 0.1%
3.852566697 1
 
< 0.1%
-612.845514 1
 
< 0.1%
-76.52310404 1
 
< 0.1%
-16.6917156 1
 
< 0.1%
-40.79919848 1
 
< 0.1%
-166.8114238 1
 
< 0.1%
-105.3951638 1
 
< 0.1%
-16.04446536 1
 
< 0.1%
-91.1792952 1
 
< 0.1%
Other values (4148) 4148
99.8%
ValueCountFrequency (%)
-8.276644654 1
 
< 0.1%
-9.298758489 1
 
< 0.1%
-835.6911905 1
 
< 0.1%
59.87858087 1
 
< 0.1%
47.23486834 1
 
< 0.1%
95.98592037 1
 
< 0.1%
-535.8220613 1
 
< 0.1%
93.84033971 1
 
< 0.1%
93.78065787 1
 
< 0.1%
80.28453442 1
 
< 0.1%
Other values (3920) 3920
99.7%
ValueCountFrequency (%)
-796.3873167 1
< 0.1%
-796.1804068 1
< 0.1%
-796.0312827 1
< 0.1%
-795.6581618 1
< 0.1%
-794.3248543 1
< 0.1%
-794.2984454 1
< 0.1%
-794.2718917 1
< 0.1%
-793.6568852 1
< 0.1%
-792.1151832 1
< 0.1%
-791.8543834 1
< 0.1%
ValueCountFrequency (%)
-900.5269515 1
< 0.1%
-900.4454491 1
< 0.1%
-900.1221424 1
< 0.1%
-899.5329937 1
< 0.1%
-899.1125735 1
< 0.1%
-898.6803786 1
< 0.1%
-895.89065 1
< 0.1%
-893.7963013 1
< 0.1%
-893.6234203 1
< 0.1%
-893.2041033 1
< 0.1%
ValueCountFrequency (%)
-900.5269515 1
< 0.1%
-900.4454491 1
< 0.1%
-900.1221424 1
< 0.1%
-899.5329937 1
< 0.1%
-899.1125735 1
< 0.1%
-898.6803786 1
< 0.1%
-895.89065 1
< 0.1%
-893.7963013 1
< 0.1%
-893.6234203 1
< 0.1%
-893.2041033 1
< 0.1%
ValueCountFrequency (%)
-796.3873167 1
< 0.1%
-796.1804068 1
< 0.1%
-796.0312827 1
< 0.1%
-795.6581618 1
< 0.1%
-794.3248543 1
< 0.1%
-794.2984454 1
< 0.1%
-794.2718917 1
< 0.1%
-793.6568852 1
< 0.1%
-792.1151832 1
< 0.1%
-791.8543834 1
< 0.1%

Ic
Real number (ℝ)

 Our DatasetCollected Dataset
Distinct41583930
Distinct (%)100.0%100.0%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean125.6996428.157201
 Our DatasetCollected Dataset
Minimum-369.79094-883.35776
Maximum647.4347901.10116
Zeros00
Zeros (%)0.0%0.0%
Negative3592012
Negative (%)8.6%51.2%
Memory size32.6 KiB30.8 KiB
2023-11-24T21:35:03.319748image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

 Our DatasetCollected Dataset
Minimum-369.79094-883.35776
5-th percentile-9.8950489-722.03139
Q19.5170263-62.836682
median18.119403-3.709479
Q3216.9724348.671071
95-th percentile550.88013769.46561
Maximum647.4347901.10116
Range1017.22561784.4589
Interquartile range (IQR)207.4554111.50775

Descriptive statistics

 Our DatasetCollected Dataset
Standard deviation176.4524372.34153
Coefficient of variation (CV)1.403762213.22367
Kurtosis0.965045460.836743
Mean125.6996428.157201
Median Absolute Deviation (MAD)30.52602554.05621
Skewness1.31842830.1208604
Sum522659.09110657.8
Variance31135.448138638.22
MonotonicityNot monotonicNot monotonic
2023-11-24T21:35:03.940210image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.968115382 1
 
< 0.1%
210.0111872 1
 
< 0.1%
603.1327913 1
 
< 0.1%
15.41400328 1
 
< 0.1%
14.63043526 1
 
< 0.1%
58.901569 1
 
< 0.1%
249.6200692 1
 
< 0.1%
16.92556438 1
 
< 0.1%
118.033108 1
 
< 0.1%
306.0330578 1
 
< 0.1%
Other values (4148) 4148
99.8%
ValueCountFrequency (%)
30.78790205 1
 
< 0.1%
42.0242242 1
 
< 0.1%
667.0235437 1
 
< 0.1%
6.458693371 1
 
< 0.1%
-87.80658173 1
 
< 0.1%
-807.643661 1
 
< 0.1%
-57.08988846 1
 
< 0.1%
-40.28158418 1
 
< 0.1%
-59.35910897 1
 
< 0.1%
-88.00694076 1
 
< 0.1%
Other values (3920) 3920
99.7%
ValueCountFrequency (%)
-369.7909376 1
< 0.1%
-358.0527173 1
< 0.1%
-346.3390674 1
< 0.1%
-329.0608228 1
< 0.1%
-319.1130771 1
< 0.1%
-315.2555292 1
< 0.1%
-309.8052793 1
< 0.1%
-306.4682365 1
< 0.1%
-297.4182071 1
< 0.1%
-289.6131182 1
< 0.1%
ValueCountFrequency (%)
-883.3577621 1
< 0.1%
-883.3450602 1
< 0.1%
-883.2003765 1
< 0.1%
-883.1143563 1
< 0.1%
-883.0008635 1
< 0.1%
-882.7488998 1
< 0.1%
-882.7292019 1
< 0.1%
-882.5739576 1
< 0.1%
-882.5692431 1
< 0.1%
-882.5435312 1
< 0.1%
ValueCountFrequency (%)
-883.3577621 1
< 0.1%
-883.3450602 1
< 0.1%
-883.2003765 1
< 0.1%
-883.1143563 1
< 0.1%
-883.0008635 1
< 0.1%
-882.7488998 1
< 0.1%
-882.7292019 1
< 0.1%
-882.5739576 1
< 0.1%
-882.5692431 1
< 0.1%
-882.5435312 1
< 0.1%
ValueCountFrequency (%)
-369.7909376 1
< 0.1%
-358.0527173 1
< 0.1%
-346.3390674 1
< 0.1%
-329.0608228 1
< 0.1%
-319.1130771 1
< 0.1%
-315.2555292 1
< 0.1%
-309.8052793 1
< 0.1%
-306.4682365 1
< 0.1%
-297.4182071 1
< 0.1%
-289.6131182 1
< 0.1%

Va
Real number (ℝ)

 Our DatasetCollected Dataset
Distinct41583930
Distinct (%)100.0%100.0%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.16409474-0.0039754245
 Our DatasetCollected Dataset
Minimum-1-0.62071235
Maximum0.758695860.59534034
Zeros00
Zeros (%)0.0%0.0%
Negative14421995
Negative (%)34.7%50.8%
Memory size32.6 KiB30.8 KiB
2023-11-24T21:35:04.489193image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

 Our DatasetCollected Dataset
Minimum-1-0.62071235
5-th percentile-0.37463317-0.54450974
Q1-0.1360254-0.12146572
median0.1805795-0.002057375
Q30.470042870.1226282
95-th percentile0.692379540.51119453
Maximum0.758695860.59534034
Range1.75869591.2160527
Interquartile range (IQR)0.606068260.24409392

Descriptive statistics

 Our DatasetCollected Dataset
Standard deviation0.365571510.29051216
Coefficient of variation (CV)2.2278076-73.077017
Kurtosis-0.64662418-0.20267247
Mean0.16409474-0.0039754245
Median Absolute Deviation (MAD)0.30232070.12333617
Skewness-0.31751817-0.066168915
Sum682.30593-15.623418
Variance0.133642530.084397317
MonotonicityNot monotonicNot monotonic
2023-11-24T21:35:05.056612image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.419107552 1
 
< 0.1%
0.14325861 1
 
< 0.1%
0.610311322 1
 
< 0.1%
0.207377317 1
 
< 0.1%
0.700962218 1
 
< 0.1%
-0.348419741 1
 
< 0.1%
0.062498305 1
 
< 0.1%
0.51847537 1
 
< 0.1%
0.50245954 1
 
< 0.1%
-0.039189176 1
 
< 0.1%
Other values (4148) 4148
99.8%
ValueCountFrequency (%)
0.522008592 1
 
< 0.1%
0.277322914 1
 
< 0.1%
-0.033106261 1
 
< 0.1%
0.200642102 1
 
< 0.1%
-0.331431991 1
 
< 0.1%
0.037998824 1
 
< 0.1%
-0.042175068 1
 
< 0.1%
-0.496784195 1
 
< 0.1%
-0.596027131 1
 
< 0.1%
-0.566470273 1
 
< 0.1%
Other values (3920) 3920
99.7%
ValueCountFrequency (%)
-1 1
< 0.1%
-0.999699721 1
< 0.1%
-0.999117634 1
< 0.1%
-0.998403699 1
< 0.1%
-0.997473801 1
< 0.1%
-0.996148749 1
< 0.1%
-0.993723758 1
< 0.1%
-0.991303439 1
< 0.1%
-0.98817554 1
< 0.1%
-0.985051944 1
< 0.1%
ValueCountFrequency (%)
-0.620712354 1
< 0.1%
-0.620634095 1
< 0.1%
-0.620370754 1
< 0.1%
-0.620187923 1
< 0.1%
-0.620152168 1
< 0.1%
-0.61981096 1
< 0.1%
-0.619708826 1
< 0.1%
-0.619632234 1
< 0.1%
-0.619536146 1
< 0.1%
-0.619428737 1
< 0.1%
ValueCountFrequency (%)
-0.620712354 1
< 0.1%
-0.620634095 1
< 0.1%
-0.620370754 1
< 0.1%
-0.620187923 1
< 0.1%
-0.620152168 1
< 0.1%
-0.61981096 1
< 0.1%
-0.619708826 1
< 0.1%
-0.619632234 1
< 0.1%
-0.619536146 1
< 0.1%
-0.619428737 1
< 0.1%
ValueCountFrequency (%)
-1 1
< 0.1%
-0.999699721 1
< 0.1%
-0.999117634 1
< 0.1%
-0.998403699 1
< 0.1%
-0.997473801 1
< 0.1%
-0.996148749 1
< 0.1%
-0.993723758 1
< 0.1%
-0.991303439 1
< 0.1%
-0.98817554 1
< 0.1%
-0.985051944 1
< 0.1%

Vb
Real number (ℝ)

 Our DatasetCollected Dataset
Distinct41583930
Distinct (%)100.0%100.0%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean-0.31639113-0.0017718407
 Our DatasetCollected Dataset
Minimum-1-0.608016
Maximum0.692943830.62787476
Zeros00
Zeros (%)0.0%0.0%
Negative33431977
Negative (%)80.4%50.3%
Memory size32.6 KiB30.8 KiB
2023-11-24T21:35:05.629565image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

 Our DatasetCollected Dataset
Minimum-1-0.608016
5-th percentile-0.68968367-0.55611313
Q1-0.5856388-0.16724858
median-0.42610265-0.0009568495
Q3-0.178689830.14941854
95-th percentile0.503849670.58410245
Maximum0.692943830.62787476
Range1.69294381.2358908
Interquartile range (IQR)0.406948970.31666712

Descriptive statistics

 Our DatasetCollected Dataset
Standard deviation0.367590120.31236482
Coefficient of variation (CV)-1.1618218-176.29396
Kurtosis0.34900142-0.36801227
Mean-0.31639113-0.0017718407
Median Absolute Deviation (MAD)0.175356660.15962968
Skewness1.14925010.055986332
Sum-1315.5543-6.9633341
Variance0.13512250.097571783
MonotonicityNot monotonicNot monotonic
2023-11-24T21:35:06.224648image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.261384086 1
 
< 0.1%
-0.424884039 1
 
< 0.1%
-0.173091918 1
 
< 0.1%
-0.641279641 1
 
< 0.1%
-0.568160927 1
 
< 0.1%
-0.248040935 1
 
< 0.1%
-0.530619627 1
 
< 0.1%
-0.697300117 1
 
< 0.1%
-0.01951673 1
 
< 0.1%
-0.585085262 1
 
< 0.1%
Other values (4148) 4148
99.8%
ValueCountFrequency (%)
-0.490542889 1
 
< 0.1%
-0.586686802 1
 
< 0.1%
-0.006324113 1
 
< 0.1%
-0.49934449 1
 
< 0.1%
0.624467589 1
 
< 0.1%
-0.035147214 1
 
< 0.1%
0.329089007 1
 
< 0.1%
-0.055046586 1
 
< 0.1%
0.169412366 1
 
< 0.1%
0.510145421 1
 
< 0.1%
Other values (3920) 3920
99.7%
ValueCountFrequency (%)
-1 1
< 0.1%
-0.99975838 1
< 0.1%
-0.999290001 1
< 0.1%
-0.998715523 1
< 0.1%
-0.997967271 1
< 0.1%
-0.99690106 1
< 0.1%
-0.994949625 1
< 0.1%
-0.993001918 1
< 0.1%
-0.990484833 1
< 0.1%
-0.987971252 1
< 0.1%
ValueCountFrequency (%)
-0.608016002 1
< 0.1%
-0.607985768 1
< 0.1%
-0.607709354 1
< 0.1%
-0.606389426 1
< 0.1%
-0.605916069 1
< 0.1%
-0.605853549 1
< 0.1%
-0.605853531 1
< 0.1%
-0.605829118 1
< 0.1%
-0.605592672 1
< 0.1%
-0.605592648 1
< 0.1%
ValueCountFrequency (%)
-0.608016002 1
< 0.1%
-0.607985768 1
< 0.1%
-0.607709354 1
< 0.1%
-0.606389426 1
< 0.1%
-0.605916069 1
< 0.1%
-0.605853549 1
< 0.1%
-0.605853531 1
< 0.1%
-0.605829118 1
< 0.1%
-0.605592672 1
< 0.1%
-0.605592648 1
< 0.1%
ValueCountFrequency (%)
-1 1
< 0.1%
-0.99975838 1
< 0.1%
-0.999290001 1
< 0.1%
-0.998715523 1
< 0.1%
-0.997967271 1
< 0.1%
-0.99690106 1
< 0.1%
-0.994949625 1
< 0.1%
-0.993001918 1
< 0.1%
-0.990484833 1
< 0.1%
-0.987971252 1
< 0.1%

Vc
Real number (ℝ)

 Our DatasetCollected Dataset
Distinct41573928
Distinct (%)> 99.9%99.9%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean0.0856079390.0057472652
 Our DatasetCollected Dataset
Minimum-0.36026137-0.61270917
Maximum10.60016665
Zeros00
Zeros (%)0.0%0.0%
Negative14581878
Negative (%)35.1%47.8%
Memory size32.6 KiB30.8 KiB
2023-11-24T21:35:06.997491image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

 Our DatasetCollected Dataset
Minimum-0.36026137-0.61270917
5-th percentile-0.33033752-0.53979499
Q1-0.15009547-0.22323875
median0.144583880.008102904
Q30.290905250.24352956
95-th percentile0.370679330.54005022
Maximum10.60016665
Range1.36026141.2128758
Interquartile range (IQR)0.441000720.46676831

Descriptive statistics

 Our DatasetCollected Dataset
Standard deviation0.253084540.3095659
Coefficient of variation (CV)2.956320953.863166
Kurtosis-0.45094749-0.63151417
Mean0.0856079390.0057472652
Median Absolute Deviation (MAD)0.177793480.23360402
Skewness-0.13082831-0.052038686
Sum355.9578122.586752
Variance0.0640517850.095831047
MonotonicityNot monotonicNot monotonic
2023-11-24T21:35:07.591112image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.359801387 2
 
< 0.1%
-0.337140579 1
 
< 0.1%
0.047566885 1
 
< 0.1%
-0.320174883 1
 
< 0.1%
0.21516587 1
 
< 0.1%
-0.321832014 1
 
< 0.1%
-0.01056085 1
 
< 0.1%
0.306131163 1
 
< 0.1%
0.322884753 1
 
< 0.1%
0.158816697 1
 
< 0.1%
Other values (4147) 4147
99.7%
ValueCountFrequency (%)
0.324040405 2
 
0.1%
0.313491296 2
 
0.1%
-0.031465703 1
 
< 0.1%
-0.332708563 1
 
< 0.1%
0.039430375 1
 
< 0.1%
0.298702387 1
 
< 0.1%
-0.293035598 1
 
< 0.1%
-0.00285161 1
 
< 0.1%
-0.286913939 1
 
< 0.1%
0.551830781 1
 
< 0.1%
Other values (3918) 3918
99.7%
ValueCountFrequency (%)
-0.360261371 1
< 0.1%
-0.360108762 1
< 0.1%
-0.360041508 1
< 0.1%
-0.360038974 1
< 0.1%
-0.359891041 1
< 0.1%
-0.359879689 1
< 0.1%
-0.359877171 1
< 0.1%
-0.359864837 1
< 0.1%
-0.359838754 1
< 0.1%
-0.359741275 1
< 0.1%
ValueCountFrequency (%)
-0.612709171 1
< 0.1%
-0.612695809 1
< 0.1%
-0.612573035 1
< 0.1%
-0.612452666 1
< 0.1%
-0.612220641 1
< 0.1%
-0.612205828 1
< 0.1%
-0.612192515 1
< 0.1%
-0.612146432 1
< 0.1%
-0.612128295 1
< 0.1%
-0.612030882 1
< 0.1%
ValueCountFrequency (%)
-0.612709171 1
< 0.1%
-0.612695809 1
< 0.1%
-0.612573035 1
< 0.1%
-0.612452666 1
< 0.1%
-0.612220641 1
< 0.1%
-0.612205828 1
< 0.1%
-0.612192515 1
< 0.1%
-0.612146432 1
< 0.1%
-0.612128295 1
< 0.1%
-0.612030882 1
< 0.1%
ValueCountFrequency (%)
-0.360261371 1
< 0.1%
-0.360108762 1
< 0.1%
-0.360041508 1
< 0.1%
-0.360038974 1
< 0.1%
-0.359891041 1
< 0.1%
-0.359879689 1
< 0.1%
-0.359877171 1
< 0.1%
-0.359864837 1
< 0.1%
-0.359838754 1
< 0.1%
-0.359741275 1
< 0.1%

Fault
Real number (ℝ)

 Our DatasetCollected Dataset
Distinct126
Distinct (%)0.3%0.2%
Missing00
Missing (%)0.0%0.0%
Infinite00
Infinite (%)0.0%0.0%
Mean776.90212479.30687
 Our DatasetCollected Dataset
Minimum00
Maximum11111111
Zeros1801182
Zeros (%)4.3%30.1%
Negative00
Negative (%)0.0%0.0%
Memory size32.6 KiB30.8 KiB
2023-11-24T21:35:08.026803image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

 Our DatasetCollected Dataset
Minimum00
5-th percentile110
Q11110
median1011111
Q311001011
95-th percentile11111111
Maximum11111111
Range11111111
Interquartile range (IQR)9891011

Descriptive statistics

 Our DatasetCollected Dataset
Standard deviation449.07358492.82614
Coefficient of variation (CV)0.57803111.0282059
Kurtosis-1.0741508-1.8826885
Mean776.90212479.30687
Median Absolute Deviation (MAD)90111
Skewness-0.931457020.26656783
Sum32303591883676
Variance201667.08242877.61
MonotonicityNot monotonicNot monotonic
2023-11-24T21:35:08.359820image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1001 712
17.1%
1100 617
14.8%
1011 541
13.0%
1110 496
11.9%
111 309
7.4%
101 277
 
6.7%
1111 244
 
5.9%
11 243
 
5.8%
1101 243
 
5.8%
0 180
 
4.3%
Other values (2) 296
7.1%
ValueCountFrequency (%)
0 1182
30.1%
1011 567
14.4%
1111 566
14.4%
1001 565
14.4%
111 548
13.9%
110 502
12.8%
ValueCountFrequency (%)
0 180
 
4.3%
11 243
 
5.8%
101 277
 
6.7%
110 179
 
4.3%
111 309
7.4%
1001 712
17.1%
1010 117
 
2.8%
1011 541
13.0%
1100 617
14.8%
1101 243
 
5.8%
ValueCountFrequency (%)
0 1182
30.1%
110 502
12.8%
111 548
13.9%
1001 565
14.4%
1011 567
14.4%
1111 566
14.4%
ValueCountFrequency (%)
0 1182
28.4%
110 502
12.1%
111 548
13.2%
1001 565
13.6%
1011 567
13.6%
1111 566
13.6%
ValueCountFrequency (%)
0 180
 
4.6%
11 243
 
6.2%
101 277
 
7.0%
110 179
 
4.6%
111 309
7.9%
1001 712
18.1%
1010 117
 
3.0%
1011 541
13.8%
1100 617
15.7%
1101 243
 
6.2%

Interactions

Our Dataset

2023-11-24T21:34:39.617262image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:55.142057image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:26.377654image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:43.045466image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:28.459272image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:45.073350image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:30.560099image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:47.039664image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:32.790295image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:49.064349image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:35.094420image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:51.267834image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:37.324631image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:53.200212image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:39.880199image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:55.403288image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:26.684768image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:43.361148image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:28.745396image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:45.359705image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:30.837532image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:47.336763image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:33.149524image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:49.359809image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:35.483871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:51.558139image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:37.602404image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:53.476101image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:40.154248image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:55.670453image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:26.928781image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:43.671997image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:29.007025image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:45.637679image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:31.097648image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:47.617930image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:33.464163image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:49.649269image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:35.804265image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:51.828719image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:37.889701image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:53.749209image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:40.430956image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:55.946790image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:27.218066image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:43.935433image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:29.279853image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:45.906791image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:31.365794image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:47.890711image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:33.806383image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:50.135176image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:36.078793image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:52.089344image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:38.166795image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:54.048862image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:40.724229image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:56.231693image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:27.577023image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:44.233322image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:29.569765image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:46.203872image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:31.671608image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:48.190796image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:34.117965image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:50.415963image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:36.384265image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:52.369768image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:38.644319image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:54.306810image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:41.022372image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:56.527399image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:27.881389image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:44.522380image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:29.858245image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:46.481443image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:32.124199image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:48.484942image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:34.485847image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:50.708380image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:36.712102image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:52.651552image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:39.025192image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:54.571278image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:41.324625image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:56.833190image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:28.176609image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:44.819918image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:30.180406image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:46.759588image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:32.492716image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:48.774837image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:34.822223image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:50.992474image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:37.024793image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:52.933082image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

2023-11-24T21:34:39.336080image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:34:54.864686image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

Our Dataset

2023-11-24T21:35:08.644789image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Collected Dataset

2023-11-24T21:35:09.129847image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Our Dataset

IaIbIcVaVbVcFaultGCBA
Ia1.000-0.362-0.2130.2230.553-0.5470.0590.2010.4530.2570.719
Ib-0.3621.000-0.375-0.511-0.1940.505-0.1770.0550.1380.6630.169
Ic-0.213-0.3751.0000.305-0.2440.0040.3510.1090.8460.3190.411
Va0.223-0.5110.3051.000-0.048-0.6980.0060.2260.1310.2300.244
Vb0.553-0.194-0.244-0.0481.000-0.5740.0660.1550.3040.2010.156
Vc-0.5470.5050.004-0.698-0.5741.000-0.0070.1660.1090.1260.108
Fault0.059-0.1770.3510.0060.066-0.0071.0000.9990.0940.1290.102
G0.2010.0550.1090.2260.1550.1660.9991.0000.0940.1290.102
C0.4530.1380.8460.1310.3040.1090.0940.0941.0000.0040.387
B0.2570.6630.3190.2300.2010.1260.1290.1290.0041.0000.014
A0.7190.1690.4110.2440.1560.1080.1020.1020.3870.0141.000

Collected Dataset

IaIbIcVaVbVcFaultGC
Ia1.000-0.331-0.1750.193-0.2620.1380.0440.6740.171
Ib-0.3311.000-0.437-0.0660.139-0.011-0.1270.1780.636
Ic-0.175-0.4371.0000.085-0.3210.2750.1670.1890.852
Va0.193-0.0660.0851.000-0.444-0.3490.0350.4540.374
Vb-0.2620.139-0.321-0.4441.000-0.615-0.0600.2250.783
Vc0.138-0.0110.275-0.349-0.6151.0000.0260.5390.532
Fault0.044-0.1270.1670.035-0.0600.0261.0000.9990.137
G0.6740.1780.1890.4540.2250.5390.9991.0000.137
C0.1710.6360.8520.3740.7830.5320.1370.1371.000

Missing values

Our Dataset

2023-11-24T21:34:41.731609image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.

Collected Dataset

2023-11-24T21:34:57.183830image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.

Our Dataset

2023-11-24T21:34:42.239954image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Collected Dataset

2023-11-24T21:34:57.606391image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Our Dataset

GCBAIaIbIcVaVbVcFault
00111-18.57624710.6081327.9681150.4191080.261384-0.337141111
10111-215.885362-145.892855361.7782170.124267-0.5578130.230202111
21001-117.837125-9.93149915.0183470.432955-0.6928220.1520971001
31011-108.040483-29.85690015.0905570.168390-0.6439690.2537831011
40101-128.6965057.041020121.656060-0.301509-0.6015580.457441101
51001-119.853142-7.95706014.6724300.391464-0.6899110.1705131001
61001-37.25805619.72431117.533745-0.234817-0.3925390.3167831001
71111-114.248003-0.655734114.903738-0.155051-0.3455020.2540261111
80111-59.3124693.66349955.648971-0.057220-0.2383610.151357111
90101-330.021027-6.581279336.6057160.576155-0.504196-0.014710101

Collected Dataset

GCIaIbIcVaVbVcFault
000-25.966805-8.27664530.7879020.522009-0.490543-0.0314660
110827.92930765.235107-14.917177-0.181270-0.0087400.1900101001
200-26.29141592.247084-68.853681-0.6160930.2766850.3394080
310849.642844-273.26564225.5280550.027473-0.3389330.3114601011
400-48.67685495.332992-49.778235-0.5519660.0487050.5032610
50051.517464-10.542698-44.3994430.2836640.329032-0.6126960
610-239.19010131.528550-48.492203-0.3478160.604109-0.2562941001
701-51.634611-737.145874790.862264-0.0386380.0042740.034364111
80156.519392-439.316108385.4421110.3002080.036604-0.336811110
910366.65526865.136839-44.599408-0.3682290.428997-0.0607681001

Our Dataset

GCBAIaIbIcVaVbVcFault
41481011-94.75664627.7361217.028309-0.286973-0.3805410.3355991011
41491111-50.476088-574.567348625.0434360.521277-0.305877-0.0914151111
41500101-254.639472-0.919287255.5617670.238683-0.5725730.182917101
41511111-18.14253610.6154997.5270370.4258260.267179-0.3433791111
41520011-18.01754510.6083667.409179-0.344702-0.3734700.35965211
41531101-72.781280-14.034463312.9299250.614483-0.134003-0.2255041101
41541100-6.4852601.166320211.3302120.309291-0.5810050.1533851100
41551111-159.179680-27.476797186.656477-0.233517-0.5584030.4023561111
41560110-18.067130-10.39104228.458172-0.419489-0.2165470.314023110
41571011-74.70348434.47304513.753214-0.099021-0.5177110.3166091011

Collected Dataset

GCIaIbIcVaVbVcFault
39200021.38122666.679004-91.204911-0.4755720.589884-0.1143120
392110-441.72184214.776044-44.010296-0.2971600.596760-0.2996001001
392210-686.380032-86.126672-37.694980-0.0104910.356343-0.3458531011
392310-392.874324-478.423453-56.213954-0.0423550.342432-0.3000771011
3924008.840961-26.77069714.6140460.590567-0.246677-0.3438900
392511-642.903092274.470331369.1342000.0292250.016884-0.0461101111
39260024.29998762.873731-90.200680-0.4561110.598154-0.1420430
392710872.028672-429.27745315.3560250.021079-0.3022820.2812031011
392800-54.16968292.261005-41.093371-0.512467-0.0281240.5405910
39290112.748262525.428472-535.4720640.4595880.029300-0.488888110

Duplicate rows

Our Dataset

GCBAIaIbIcVaVbVcFault# duplicates
Dataset does not contain duplicate rows.

Collected Dataset

GCIaIbIcVaVbVcFault# duplicates
Dataset does not contain duplicate rows.